The Tool Desk
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What a token budget needs to cover
Tokens are the units models use to process text, but a request’s size is not just the latest message. Depending on the model and API, the context window can include input, generated output, and reasoning tokens. OpenAI defines the context window as the maximum number of tokens usable in a single request and warns that oversized prompts and accumulated turns can exceed the allocation and lead to truncation: OpenAI’s conversation-state guide.
An output limit is a ceiling on generated tokens. It does not tell you whether the input plus output fits the context window. Reasoning may also consume capacity, with its treatment depending on the model and interface. A low output cap can end an answer before it is complete; a large cap cannot make an overlong input fit.
| Control | What it governs | What to check |
|---|---|---|
| Context window | Total token capacity for a request; accounting can include input, output, and reasoning. | The exact model’s current context documentation. |
| Output limit | Maximum generated tokens for a response. | The endpoint’s parameter name, semantics, and model-specific ceiling. |
| Reasoning control or allowance | How reasoning effort or tokens are handled, where supported. | Current model guidance; controls differ, and some newer models use adaptive effort rather than a manually set token budget. |
| Agent task budget | An advisory budget that may span an agent loop, including thinking, tool calls, tool results, and output. | Whether the provider supports it and how it differs from the enforced per-response cap. |
Anthropic describes its task budget as advisory across an agent loop, while max_tokens still enforces the per-response ceiling: Anthropic’s task-budget documentation. These controls should not be treated as interchangeable.
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How to set a budget for a specific request
- Choose the exact model and interface. Note the model identifier or version, API or product endpoint, current context window, and maximum output. Limits and parameter meanings vary by provider and model, so do not carry a count or ceiling over from a different model.
- Assemble the complete request. Include system and developer instructions, the current user message, retained conversation history, examples, tool or function definitions, and structured or multimodal inputs. Count what will actually be sent, not only the visible text in the newest turn.
- Count with the target provider’s method. Use its tokenizer or token-counting endpoint for the selected model and request type. The count may reflect provider-added material: Anthropic notes that its counting endpoint can include tokens added automatically for system optimizations.
- Decide how much answer capacity the task needs. A short classification needs less output capacity than a detailed report. Set the response limit accordingly, and account for reasoning if the selected model’s semantics require it. There is no official, universal input-to-output ratio that works for every task.
- Leave headroom. Do not plan right up to the published maximum. Serialization details, provider-added tokens, variable answer length, or added context can push a request over the limit. If the budget is tight, remove low-value context, summarize older turns, retrieve only relevant passages, reduce tool payloads, or select a suitable larger-context model.
- Recount when the request changes. Recalculate after switching models, adding tools, extending history, or introducing media. Where the provider returns actual usage, compare it with your estimate and adjust subsequent budgets.
How to count the whole prompt
Use the provider’s current method rather than a generic words-to-tokens estimate. Tokenizers differ, and structured request formats can add content that is not apparent in a plain-text view. OpenAI provides a tokenizer and input-token counting guidance for complete Responses API inputs: OpenAI’s conversation-state guide and OpenAI’s token-counting help page. Anthropic offers model-specific counting: Claude token-counting documentation. Google provides token counting through the Gemini API: Gemini token guide.
For a useful count, include all content and request components that will be sent. Tool definitions can take space even when a tool is not called in the eventual answer. Images, audio, video, and other supported media are also tokenized; text length alone cannot capture their cost. Google notes, for example, that image tiling affects image-token accounting. Count with the actual model and API rather than estimating media as if it were ordinary text.
Budgeting conversations that retain history
If an application resends the conversation on each turn, earlier messages remain part of the current request. A short follow-up question can therefore sit on top of a large prompt. Keep the history that affects the answer, but summarize or compact older turns when they no longer need to be reproduced verbatim. OpenAI’s conversation guidance advises accounting for accumulated turns and added context: conversation state.
Do not assume that summing every token ever transmitted is the same as a provider’s agent task budget. A task-wide budget may track a particular agent loop and its compaction behavior, while each request has its own input and output accounting. Check the provider’s definition of each measure before using it for monitoring or enforcement.
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When comparing providers or model options, check the same dimensions for each one:
- Exact model and version, including any endpoint-specific distinction.
- Total context capacity versus maximum generated output.
- Availability of complete-request token counting.
- How reasoning, tools, and retained conversation history are accounted for.
- Supported modalities and how their token use is counted.
- Whether a budget is advisory or a hard-enforced limit.
Provider terminology can differ. OpenAI describes context as including input, output, and reasoning; Google describes the Gemini context window as the combined input/output limit; Anthropic distinguishes its advisory task-wide budget from the hard response cap. Confirm the current model documentation before relying on a numerical limit.
For scale only, OpenAI’s documentation gives GPT-4o-2024-08-06 as an example with a 128k context window and a 16,384-token maximum output. Those figures describe that named model-version example, not a general or current limit for all OpenAI models. Verify the live documentation for the model you intend to use.
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